The following relates to the demand prediction arts such as off-street parking demand prediction, predictive modeling of time series data, and related arts.
Some illustrative examples of demand prediction problems include predicting demand for off-street parking, predicting demand for a commercial product (e.g. gasoline, electrical power, a retail merchandise product), and so forth. Typically, such demand follows certain discernible patterns. For example, demand for off-street parking can be expected to exhibit daily cycles (e.g., parking usage as measured by parking ticket issuances may be expected to be highest in the morning as commuters arrive and lower during the day, with the lowest usage the midnight-early a.m. time interval) weekly cycles (e.g. high parking usage during the workweek and lower usage on weekends, or vice versa for parking facilities that mostly service weekend clientele), and longer seasonal cycles (e.g., a parking facility that services a university may be heavily used when the university is in session and lightly used between sessions).
By recognizing such cycles, a parking lot operator can plan ahead in terms of scheduling parking lot attendants for work, optimizing parking prices based on predicted demand, and so forth. Additionally, the parking lot operator may take known special events into account, for example a parking facility located near a football stadium may be expected to see heavy usage just prior to a football game held at the stadium.